Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Networks

In wireless sensor networks (WSNs), the protocols for routing have a great effect in performance of network as network’s lifetime, good energy organization, etc. These protocols are developed based on the different schemes like clustering, chaining and cost based. The WSN contains a large number of nodes which are sometimes difficult to manage. So, the best way is to make a cluster by combining various nodes, and this technique is known as clustering. By doing so, the energy exhaustion by nodes can be restricted. A node is nominated as cluster head (CH) to handle the communication amongst nodes and managing of nodes in the cluster. An ACO-based probability rule is used for choosing the CH amid the cluster nodes. The data are sent from cluster nodes to the CH; then, it further sends the relevant details to the base station (BS). ACO-DEEC: ant colony optimization-based distributed energy efficient clustering protocol is used for probability rule calculation for CH selection depending on the metrics, i.e. efficiency of the nodes and distance amongst nodes. The algorithm proposed enhances the parameters like energy consumption by cluster nodes, finding dead nodes in cluster and quantity of packets sent to the BS as compared with existing DEEC protocol.KeywordsACO-DEECAnt Colony optimizationCluster headDEEC protocolWireless sensor network

In wireless sensor networks, there has been a huge number of sensor nodes and all are handled with limited potential supplied by the battery source. The nodes in the network use energy to sense and process the information collected from the environment and communicate it to the Base Station. It is assigned with the problem to decrease the energy consumption, a number of routing and clustering protocols have been proposed so as to get large energy efficiency in heterogeneous network structure. Clustering plays a significant and important role to control the energy consumption at the nodes, as the routing and transfer of data. It is done by the cluster heads and hence prolongs the network lifetime. we proposed dispense and energy conscious clustering protocols that follow the thoughts of Distributed Energy Efficient Clustering protocol which produces a better results as compared to DCHRP, ETSSEP, TSEP and SEP.

Improving network lifetime is an important challenge in Heterogeneous Wireless sensor Networks. The best way to improve a heterogeneous WSN lifetime is to regrouping the network by the clusters which each one is managed by a leader called Cluster Head. The election of cluster head is based on a predefined probability that each node becomes cluster head and other nodes must join their Cluster Heads to send them their data. These cluster members can be closer to the base station and must send data to their manager, so there is a loss of energy in the network. In this paper, we propose a new technique to improve a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding the closer nodes to the base station in the clustering process. We demonstrate by simulation in MATLAB that the proposed approach increase the lifetime by around 46% and number of the received packet messages by around 184% than current heterogeneous WSN protocols as TDEEC.

Artificial Bee Colony (ABC) optimization algorithm has captured much attention of researchers from various fields, in recent times. Moreover, various comparative studies clearly reports robust convergence of ABC algorithm than other bio-inspired optimization algorithms. Nevertheless, like other optimization algorithms, ABC suffers from slower convergence and tendency towards local optima trappings. Therefore, various amendments have been proposed to avertthe flaws of ABC algorithm. Nonetheless, the variants are either computationally intensive or could not avert the flaws of the algorithms. Hence, this research work proposes an efficient variant of ABC algorithm. The proposed variant capitalizes on the global-best food-source. The proposed variant has been compared with various existing variants of ABC algorithm on a few benchmark functions. Significance of the proposed variant has also been analyzed statistically. Results show the best convergence of the proposed variant among all the compared optimization algorithms on all benchmark functions.

Wireless sensor network (WSN) plays a significant role in various commercial, industrial and agriculture sector in wide ranges of applications. Routing is necessary to maintain the reliable communication between different nodes, cluster heads, and base station; however, performance of routing mechanism is challenging due to network lifetime, dynamic nodes, higher packet drop, scalability issue, limited adaptability and environmental conditions. This paper presents various routing techniques used in WSN that covers the brief overview of flat routing, hierarchical routing, location-based routing and bio-inspired routing techniques such as Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABC) algorithms. It focuses on the routing mechanism, routing scenario, mobility, scalability, energy consumption, data aggregation, performance evaluation metrics, challenges and constraints of the routing in WSN. This comprehensive survey provides the future direction for the improvement in routing mechanisms in WSN.KeywordsBio-inspired routingClusteringFlat routingHierarchical routingRoutingWireless sensor network

Wireless Sensor Networks (WSNs) are a component of various applications, including environmental monitoring and the Internet of Things (IoT). Energy efficiency is one of the significant issues in WSNs, since sensor nodes are usually battery-powered and have limited energy resources. The Enhanced Distributed Energy-Efficient Clustering (DEEC) protocol is one of the most common methods for improving energy efficiency and network lifespan by selecting cluster heads according to residual energy. Nevertheless, standard DEEC methods are limited in dynamic environments because of their fixed nature. This paper presents a novel modular implementation of the DEEC protocol for Wireless Sensor Networks, addressing the limitations of the standard DEEC in dynamic and heterogeneous environments. Unlike the typical DEEC protocol, the proposed approach incorporates realistic energy models, supports heterogeneous nodes, implements load balancing, and enables dynamic cluster head selection Numerical simulations in MATLAB® demonstrate that the improved DEEC protocol achieves a 133% longer stability period (first node death at 1166 rounds vs. 472 rounds), nearly doubles the network lifetime (4000 rounds vs. 2111 rounds), and significantly enhances energy efficiency compared to the standard DEEC. These results verify the effectiveness of the proposed enhancements, making the protocol a robust solution for modern WSN and IoT applications.

The very challenging requirement of WSN is energy efficiency, which depends upon the distributed design and dynamic topology of the network. This requirement can be fulfilled by the multi-hop energy efficient routing protocols. The objective of all the energy efficient protocols is to extend the network’s lifetime. Sensor nodes are battery operated, so generally concern of all routing protocols is to conserve energy. In this chapter, energy efficient protocols are categorized into two types (i) heterogeneity based energy efficient routing protocols and (ii) chain based energy efficient routing protocols. Comparisons of various energy efficient routing protocols are specified here with open issues. A systematic and comprehensive taxonomy of various energy aware schemes are discussed in depth. This chapter is focused on various energy conservation schemes and hence, discussion of various routing protocols gives the readers a new insight.

The routing protocol is one of the most important aspects of the wireless sensor network, determining network connectivity, delay, energy balancing, reliability and other factors. Furthermore, a minimum hop-count does not mean minimum delay and energy consumption. To overcome the routing protocol's shortcomings, we proposed an energy-efficient expected transmission count routing decision strategy (E3TX) for wireless sensor networks. E3TX makes use of two physical parameters, received signal strength indicator (RSSI) and battery voltage, to obtain the final decision via our proposed decision strategy. In our strategy, the received signal strength indicator is used to predict the future packet reception rate (PRR), and the battery voltage is used to estimate the residual energy of network nodes to balance their load. In order to estimate the packet reception rate via the received signal strength indicator with greater accuracy, we performed multiple experiments to build the relationship model between RSSI and PRR. In this article, we use NS-2.35 to evaluate and compare the performance of E3TX with AODV, AOMDV and BIETX. Our simulation results show that our proposed E3TX performs well when compared to the previous studies, not only in terms of energy consumption, but also in reliable data transmission and end-to-end delay.

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems .

Energy is one of the most important resources in wireless sensor networks, and data transmission is a main energy consumers. So energy efficient communication protocols are very important to sensor networks. It is an essential issue in the research on energy efficient protocols that how long a sensor network can survive to the best advantage. We formalize the lifetime-maximizing problem to a multi-source multi-sink flow-maximizing problem on a directed graph with arc and vertex capacity powers. Then we propose a method to solve this kind of maximum flow problem. The method gives the value of maximum lifetime exactly and its computing complexity is low

À Load-balancing and Self-adaptation Clustering for Lifetime Prolonging in Large Scale Wireless Sensor Networks

The greatest challenge in wireless sensor networks is the constraint on energy. Because of limited communication range, message routing must be done by sensors until the information reaches the sink, resulting in large energy consumption. Sink mobility is one of the most comprehensive trends for information gathering in sensor networks. This way has a prominent role in balancing the energy consumption and culling the hotspots problem of sensor networks. In this paper, a well planned moving strategy for a mobile sink in hierarchical sensor networks is presented and PSO algorithm is applied to find the best location of the sink based on the most efficient energy consumption for data delivery of CHs. Simulation results are provided to prove the efficiency of this technique.

In this paper, the joint optimization of information transmission and energy utilization in energy harvesting wireless sensor networks (EH-WSNs) is studied. In EH-WSNs, the imbalance of energy harvesting and consumption affects the sensing and communication ability of the network. To solve this problem, a novel energy aware opportunistic routing protocol with long-short term memory (LSTM) based solar energy prediction is proposed. The protocol innovatively takes account of the nodes’ current residual energy and the harvesting solar energy in a short term which is predicted by a LSTM neural network as key factors in forwarding candidates election process of the opportunistic routing. Additionally, in order to jointly optimize the energy consumption and information transmission, a new metric with combined energy factor and relay efficiency for each node is proposed to assist candidate node selection where the relay priority considers the residual energy and working history of nodes. Simulation results show that the proposed protocol increases the network throughput by 12% and reduces retransmission rate by 15% compared to opportunistic routing based on geography. Simultaneously, it has stronger ability to balance energy consumption among sensor nodes.

Sensor nodes have severe energy constraints. The typical many-to-one traffic pattern in sensor networks causes uneven energy consumption among sensor nodes, i.e., sensor nodes near a sink or cluster head have much heavier traffic burden and run out of power much faster than other nodes. This can dramatically reduce sensor network lifetime. In this paper, we adopt a heterogeneous sensor network model and propose a novel Chessboard Clustering (CC) scheme. We also design an efficient routing protocol based on the CC scheme. Extensive simulations show that our scheme balances node energy consumption very well and significantly increases network lifetime.